With reference to FIG. 1, a ducted fan gas turbine engine generally indicated at 10 has a principal and rotational axis X-X. The engine comprises, in axial flow series, an air intake 11, a propulsive fan 12, an intermediate pressure compressor 13, a high-pressure compressor 14, combustion equipment 15, a high-pressure turbine 16, and intermediate pressure turbine 17, a low-pressure turbine 18 and a core engine exhaust nozzle 19. A nacelle 21 generally surrounds the engine 10 and defines the intake 11, a bypass duct 22 and a bypass exhaust nozzle 23.
The gas turbine engine 10 works in a conventional manner so that air entering the intake 11 is accelerated by the fan 12 to produce two air flows: a first air flow A into the intermediate pressure compressor 14 and a second air flow B which passes through the bypass duct 22 to provide propulsive thrust. The intermediate pressure compressor 13 compresses the air flow A directed into it before delivering that air to the high pressure compressor 14 where further compression takes place.
The compressed air exhausted from the high-pressure compressor 14 is directed into the combustion equipment 15 where it is mixed with fuel and the mixture combusted. The resultant hot combustion products then expand through, and thereby drive the high, intermediate and low-pressure turbines 16, 17, 18 before being exhausted through the nozzle 19 to provide additional propulsive thrust. The high, intermediate and low-pressure turbines respectively drive the high and intermediate pressure compressors 14, 13 and the fan 12 by suitable interconnecting shafts.
Engine operating conditions vary throughout a flight as e.g. ambient temperature, pressure, Mach number and power output level vary. Variation of any of these conditions can cause the engine dynamics to respond in a significantly nonlinear manner. Further, it is also necessary to ensure that the engine operates safely within its own limits. Thus an aim of engine control is thus to obtain optimum engine efficiency for a given operating condition while respecting operational constraints.
Modern engines are based on digital electronics, the collection of control system elements often being referred to as a Full Authority Digital Electronic Controller (FADEC). At the heart of the FADEC is an Engine Electronic Controller (EEC). The EEC receives measurements from onboard sensors, the measurements providing data on engine performance variables, such as gas pressures and temperatures, and on engine state variables, such as rotor speeds and metal temperatures. The EEC then uses these measurements to determine values for engine control variables, such as fuel flow, valve positions, inlet guide vane position, in accordance with a desired power output, but while staying within operational constraints. Although the engine may display nonlinear behaviour, the EEC can seek to achieve optimum values for the engine control variables on the basis, for example, of numerous linear controllers acting in concert.
While this approach has been applied successfully, engine control closer to the optimum may be obtained by the application of predictive control methodologies which take better account of engine nonlinearities.
For example, US 2006/0282177 proposes using quadratic programming for predictive control. More particularly, an algorithm is proposed that provides an interior point method for real-time implementation. The basic method comprises: linearizing a nonlinear model of a gas turbine engine, formulating the quadratic programming problem, solving the problem using the interior point method to compute the control action, and executing the control action.
EP 1538319 describes the application of nonlinear predictive control for a gas turbine application where the aim is to minimize a certain performance index. However, unlike US 2006/0282177, EP 1538319 utilizes a reduced order nonlinear model of a gas turbine engine. To avoid using nonlinear programming, the cost to be optimised is modified to include an exponential term that produces a high penalty when the predicted actuator commands and engine states are near their operating limits. This ensures that the choice of control inputs does not violate engine constraints. To minimize the cost, the patent makes use of an iterative gradient descent approach. As the reduced nonlinear model needs to know the state the engine is in (i.e. all pressures, temperatures etc.) a Kalman filter is used to estimate the unmeasured states from the measured outputs available from the sensors on the engine.
A disadvantage of US 2006/0282177 is its reliance on quadratic programming. Although quadratic programming is guaranteed to converge from a theoretical viewpoint, it is an iterative procedure without a bound on the number of steps required to reach a solution. Therefore implementation of such a controller in a real-time environment for an aerospace gas turbine application presents challenges due to the non-deterministic end time. If the algorithm does not converge by a given set time, then the controller has to use whatever solution has been reached by that time. This solution is likely to be sub-optimal, may even not be feasible, and could potentially lead to instability. Thus a controller based on the US 2006/0282177 faces significant certification challenges.
The scheme presented in EP 1538319 also suffers from the disadvantage raised above, as it still relies on an iterative algorithm—albeit a simpler one. In addition, EP 1538319 relies on the extended Kalman filter, which can also suffer from non-convergence issues. Such a scheme would require significant amounts of tuning and would lead to certification challenges due to the non-deterministic end time.